Shuxiang Xu
Papers
4
Total Citations
93
H-Index
3
About
Shuxiang Xu is a leading researcher at the intersection of computer vision, robotics, and machine learning, with a focus on advancing 3D perception and intelligent automation. Their most cited work, "A Comprehensive Review on 3D Object Detection and 6D Pose Estimation With Deep Learning" (2021, 83 citations), provides a seminal survey that has become a key reference for researchers working on spatial understanding and object manipulation in robotics and augmented reality. Xu’s contributions extend to practical industrial applications, as demonstrated in their 2024 study on spatial error prediction for industrial robots using Support Vector Regression, which achieved a dramatic reduction in positioning error from 0.706 mm to just 0.056 mm—a tenfold improvement critical for high-precision manufacturing. In the domain of data science, Xu has tackled the challenge of imbalanced datasets with an advanced SVM-based balancing method (2019), while their most recent work (2025) explores self-improving Vision-Language Models for embodied visual tracking, pushing the boundaries of how AI systems recover from failure in dynamic environments. With a career marked by both foundational reviews and cutting-edge applications, Xu continues to shape the future of intelligent robotic systems and 3D vision.
Research Focus
Key Achievements
Top Papers
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- 2Advanced Data Balancing Method with SVM Decision Boundary and Bagging5 citations · 2019
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